Diagnosis of Parkinson&#39;s disease

ABSTRACT

A method of diagnosing Parkinson&#39;s disease uses the abnormal concentrations of a group of 21 blood serum protein biomarkers. The concentration of the 21 protein biomarkers is assessed in a patient&#39;s serum by quantitative two-dimensional polyacrylamide gel electrophoresis.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application, pursuant to 35 U.S.C. 111(b), claims the benefit of the earlier filing date of provisional application Ser. No. 61/268,235 filed Jun. 10, 2009, and entitled “Diagnosis of Early Stage Parkinson's Disease: Abnormal Blood Serum Concentrations of a Select Group of Protein Biomarkers.”

FIELD OF THE INVENTION

The present invention relates in general to a method for diagnosing Parkinson's disease. In particular, the present invention relates a set of biomarkers and their use in the diagnosis of Parkinson's disease in humans.

DESCRIPTION OF THE RELATED ART

Parkinson's disease is a progressive and degenerative neurological disorder involving the loss of dopaminergic neurons in the substantia nigra of the brain. The patient looses the ability to direct or control movement in a normal manner. Parkinson's disease (PD) is a common disease among individuals over the age of 50. About 50% of the individual report symptoms occurring prior to that age and are classified as having early-onset Parkinson's disease.

A variety of medications provide some relief from the symptoms, but no drug can stop the progression of the disease. In some cases, surgery is an appropriate treatment, but typically physical therapy and muscle strengthening exercises are recommended by the physician.

The following symptoms are considered the signs of Parkinson's disease: (1) rigidity, stiffness or inflexibility of limbs and joints; (2) bradykinesis (an abnormal slowness of movement) or akinesis (the absence of movement); (3) tremors (the involuntary, regular, rhythmic shaking of a limb, the head, the mouth, the tongue or the whole body); and (4) postural instability (an impaired balance or coordination).

Signs of Parkinsonism are often considered to be benign symptoms of aging. However, Parkinsonism is prevalent among people over the age of 65 in the general population. Studies show that 15% of people between the ages of 65-74 have Parkinsonism, and increasing age correlates with an increased percentage of Parkinsonism (i.e., 30% between the ages of 75-84, and over 50% in people age 85 and over) (1).

Parkinson's disease is difficult to diagnose and of the people in the general population with Parkinsonism only about 10% received a clinical diagnosis of Parkinson's disease (1). Yet the presence of Parkinsonism gives roughly a two fold increase in the risk of death (2).

There are a number of complication is diagnosing Parkinson's disease. One complication is that other conditions such as Alzheimer's disease (AD), sub-cortical vascular disease and multisystem atrophy can cause Parkinsonism (1). Furthermore, people with mild Parkinsonism often do not seek medical attention and if they do seek medical attention their physicians may not be able to accurately diagnose Parkinsonism or mild Parkinson's disease.

In general, by the time a person receives a diagnosis of Parkinson's disease, it is likely that substantial irreversible neurological damage has already occurred; rendering treatment less effective than if these people were brought to medical attention earlier. Physicians, clinicians, and patients would benefit greatly from a quick, early stage, and accurate diagnosis.

Multiple blood tests have been evaluated for PD diagnosis, including mitochondrial complex I, markers of oxidative stress, and dopamine metabolism (52-53), but these blood tests have not proven to be robust. The expression of individual genes has also been assessed in peripheral blood. Proteasome activity related to caspase-3 activation has been shown to be decreased in PD but not in AD patients (54-55). Molecular signatures of transcripts and protein levels in peripheral blood may serve as biomarkers for Huntington disease (56), AD, and amyotrophic lateral sclerosis (ALS) (22-25, 57-63). Proteomic profiling in AD CSF and blood by mass spectrometry of small peptides, derived from biomarker proteins by proteolysis, can discriminate between AD individuals and normal controls (54, 55, and 64), but provide limited information about the proteins and pathophysiological processes involved (21).

However, to date no blood test has been proven to be consistently reliable, specific and sensitive in the diagnosis of PD. Thus, despite the extensive information available regarding aspects of the pathogenesis of Parkinson's disease (PD) an accurate clinical diagnosis and staging of the disease remain challenging and misdiagnosis occurs in about 10 to 30% of patients, with early stages being especially prone to misdiagnosis (3-5).

Given the clinical and biochemical complexity of Parkinson's disease, the difficulty in its diagnosis and the inaccessibility of the brain to repeated sampling, there is a continuing need to identify a set of biomarkers to serve as indicators or sensors of the underlying biological process.

SUMMARY OF THE INVENTION

The present invention relates in general to a method for diagnosing Parkinson's disease. In particular, the present invention relates a set of biomarkers and their use in the diagnosis of Parkinson's disease in humans.

One embodiment of the invention is a method of diagnosing Parkinson's disease, the method comprising: collecting a serum sample from a test subject; analyzing the serum sample for a change in expression of a set of protein biomarkers; and using the change in expression of the set of biomarkers to diagnose the test subject.

Another embodiment of the invention is a set of biomarkers for diagnosing Parkinson's disease, wherein a change in the expression of the set of biomarkers in a serum sample of a test subject determines a positive or a negative diagnosis of Parkinson's disease in the test subject.

Yet another embodiment of the invention is a set of biomarkers comprising: Chain A Albumin mutant R218H protein, Haptoglobin HP-2a protein, Complement Factor I protein, Apolipoprotein E3 protein, Transthyretin dimer protein, Nucleoporin NUP 188 protein, Haptoglobin HP-1 protein, Albumin protein PRO2044, Parkinson's LB acidic H2A protein, Apolipoprotein A-IV protein, Transthyretin Huntington Interacting Protein E, Complement C4b gamma chain protein, Chain A Albumin mutant R218H protein, Fidgitin I protein, Immunoglobulin kappa light chain protein, Complement Factor H/Hs protein, Fidgitin II protein, Albumin protein PRO2675, and Haptoglobin related protein.

Still another embodiment of the invention is a method for diagnosing Parkinson's disease comprising: obtaining a serum sample from a test subject; electrophoresing a portion of the serum sample on a 2D gel, wherein the portion of the serum sample has a standardized protein concentration; determining a pixel cell density for each of at least twenty-one protein biomarkers in the 2D gel; and comparing the pixel cell density of each biomarker with a mean cell pixel density determined for control serum; whereby a variation in the pixel cell density of at least twenty-one biomarkers from a mean pixel cell density of the at least twenty-one biomarkers is a positive diagnosis of Parkinson's disease.

The foregoing has outlined rather broadly several aspects of the present invention in order that the detailed description of the invention that follows may be better understood. Additional features and advantages of the invention will be described hereinafter which form the subject of the claims of the invention. It should be appreciated by those skilled in the art that the conception and the specific embodiment disclosed might be readily utilized as a basis for modifying or redesigning the structures for carrying out the same purposes as the invention. It should be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

FIG. 1 is a digital fluorescent image of blood serum proteins resolved by 2-dimensional polyacrylamide gen electrophoresis. The 21 Step Disk Biomarkers are indicated by numbers and circles.

FIGS. 2A and 2B show a Box and Whiskers, Receiver Operator Characteristics and multivariate linear discriminant biostatistical analysis.

FIG. 3 illustrates the individual statistically significant abnormal concentrations in the 21 selected biomarkers in patients with mild and moderate-severe PD symptoms.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Parkinson's disease (PD) is a progressive multisystem neurodegenerative disorder, an α-synucleinopathy, in which dopaminergic cell death exceeds a critical threshold [27, 28], and olfactory, autonomic, gastrointestinal dysfunction, dementia, depression, and sleep disorder sometimes appear prior to motor manifestations (29-30). PD has a long pre symptomatic phase where dopamine homeostasis compensates for dopaminergic neuronal loss. Breakdown of dopamine homeostasis results in alteration of basal ganglia output structures, and emergence of symptoms (31-32). PD is associated with multiple transmitter dysfunctions in dopamine, GABA, glutamate, acetylcholine, and enkephalin systems in the CNS (29, 33), in multiple brain regions, and peripheral tissues (34, 35). These findings provide support for the hypothesis that PD is not only a multi-neurotransmitter disorder but also a multi-system disorder.

The analysis of familial Parkinsonism has revealed that a wide spectrum of different types of gene mutations are associated with familial Parkinson's disease, including mutations in the α-synuclein (6-8, 36), parkin (37, 38), UCHL-1 (39)], DJ-1 (40), PINK1 (41, 42), LRKK2 (43, 44) Omi/Htra2 (45) and FBXO7 (46) genes. Differences in transcription and translation of non mutant forms of these genes, post-synthetic processing of their products, and additional molecular pathways, including mitochondrial function, protein turnover, oxidative stress, and inflammation, underlie similar manifestations of sporadic PD (47-51).

Despite the extensive information available regarding aspects of the disease pathogenesis, a consistently accurate method for the clinical diagnosis and staging of the disease is currently unavailable. For example, it has been reported that PD is misdiagnosed in about 10% to about 30% of patients, with the early stages of PD being particularly prone to misdiagnosis (3-5).

Given the complexity of PD, the difficulty in diagnosis and the inaccessibility of the nervous tissue, particularly the brain, to repeated sampling; the development of accessible biomarkers which can serve as indicators or sensors of the underlying pathophysiological processes is needed. From the extensive motor, cognitive, psychiatric, and autonomic symptoms, it is clear that multiple brain regions and peripheral tissues are affected in PD. Thus, the possibility that certain alterations in protein biomarkers can be used to diagnose PD was investigated.

The identification of biochemical markers from tissues, such as blood, that are easily accessible and accurately measured would represent a great advance in the diagnosis and treatment of PD. Since blood is the most accessible and routine physical source of biological material available for diagnostic testing, serum samples were mined for biomarkers for PD diagnostic development and testing.

Embodiments of the invention assessed the expression of numerous proteins found in serum as biomarkers for PD. This protein assessment was performed by two-dimensional polyacrylamide gel electrophoresis (2D gel electrophoresis).

2D gel electrophoresis has been used in research laboratories for biomarker discovery since the 1970's (9-18). In the past, this method has been considered highly specialized, labor intensive and non-reproducible to be applicable for diagnostic purposed. Only recently with the advent of integrated supplies, robotics, advances in software, as well as progress in data mining and bioinformatics has progression of this proteomic technique become feasible for consideration in diagnostics.

The promise and utility of 2D gel electrophoresis is based on its ability to detect changes in the expression of intact proteins and to separate and discriminate between specific intact protein isoforms that arise due to variations in amino acid sequence and/or post-synthetic protein modifications such as phosphorylation, ubiquitination, conjugation with ubiquitin-like proteins, acetylation, glycosylation, and proteolytic processing. These post-synthetic protein modifications and processes are critical features in cellular and physiological regulation, and are reflected by differentially expressed blood serum biomarkers in neurodegenerative diseases, including Alzheimer's and Parkinson's diseases, and ALS (24-25, 54-55).

Using a 2D gel electrophoresis proteomics platform (19-21), a combination of 59 specific biomarkers was found that distinguished the disease status in three neurodegenerative diseases: Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) (22-25) in retrospective stored samples from Houston, Tex., USA. A statistical model (i.e., a multivariate linear discriminant biostatistical analysis) was used to assess the concentrations of a set of protein biomarkers in the serum and to provide a probability score assigned to each blood sample that reflects the patient's disease status (23-25). This biomarker set was then assessed in a population of PD patients and age and ethnically matched controls from the region of Thessaly, Greece and cross validated with PD patients from Sun City, Ariz., USA.

Selection and Clinical Evaluation of Patient Subjects

Patients and age-matched controls were from three clinical sites: Baylor College of Medicine, Houston, Tex., USA (site 1); University of Thessaly, Larissa, Greece (site 2); and Banner Sun Health Research Institute, Sun City, Ariz., USA (site 3). The numbers of patients and controls for retrospective and prospective samples are listed in Table 1.

The study compared biomarker concentrations in serum samples of healthy participants and those with neurodegenerative diseases in the initial biomarker panel identification (site 1), and with PD in the extended investigation of the panel (sites 2 and 3).

All control subjects were healthy and had no family history of Parkinson's disease; while all PD subjects presented with at least three of the cardinal signs of idiopathic PD (i.e., resting tremor, bradykinesia, rigidity, postural instability, and response to levodopa or dopamine agonists).

TABLE 1 Patient and Control Samples Blood Serum Number of Sample Type Patients Disease Status Retrospective 115 Alzheimer's disease (AD) Stored Samples^(¥) 29 Parkinson's disease (PD) 24 AD/PD-like and Mixed^(†) 75 AD/PD Age-matched Normal Controls 136 Amyotrophic lateral sclerosis (ALS) 33 ALS-like^(‡) 57 ALS Age-matched Normal Controls Prospective Newly 62 PD Drawn Samples^(§) 78 AD/PD Age-matched Normal Controls ^(†)AD/PD-like disorders including Frontotemporal dementia; Lewy body dementia; Vascular (Multi-infarct) dementia; Alcohol related dementia; Semantic dementia; Stroke (CVA); Post-irradiation Encephalopathy and Seizures; Vascular (Multi-infarct) parkinsonism; Multiple system atrophy; Essential tremor; Corticalbasal ganglionic degeneration; and mixed disorders including Alzheimer's disease combined with Vascular (Multi-Infarct) dementia; Alzheimer's disease combined with Lewy body dementia; Parkinson's disease combined with Lewy body dementia; Alzheimer's and Parkinson's disease combined with Lewy body dementia; Frontotemporal dementia combined with Chronic inflammatory demyelinating polyneuropathy; and Thalamic CVA combined with HX of Lung CA. ^(‡)Non-ALS disorders of motor neurons, muscles, nerves, and spinal cord. ^(¥)From Houston, TX, USA. ^(§)From Thessaly, Greece and Sun City, AZ, USA

Patients were excluded from the study if they exhibited: (1) causes of secondary Parkinsonism, including vascular Parkinsonism, encephalitis, exposure to neuroleptics, or the presence of additional signs such as dementia (MMSE<25), gaze palsy, amyotrophy, cerebellar signs, or symptomatic orthostatic hypotension (mean arterial pressure drop >20 mm Hg from recumbent to standing position); (2) an unstable medical condition; (3) a history of substance abuse; (4) major depression (Hamilton score>19); (5) a history of malignant melanoma; (6) an inability to understand the consent form; or (6) a history of drug or alcohol abuse.

The medical and demographic history of all subjects was evaluated, including the following patient information: (1) date of birth; (2) sex; (3) race; (4) date of blood sample collection; (5) rigidity; (6) bradykinesia/akinesia; (7) tremors; (8) postural instability; (9) history of past illness; (10) details of all current health problems; and (11) a copy of conventional images (CAT, PET scans, MRI of brain, SPECT, etc). All forms and copies of reports were identified by study number only in order to maintain confidentiality; a copy of the above mentioned medical information was sent to the testing site in accordance with Health Information Privacy concerns.

PD patients underwent clinical evaluation to provide clinical data, including the severity of PD symptoms, according to the Hoehn and Yahr Scale (2) and the Unified Parkinson's Disease Rating Scale (UPDRS) (26).

Sample Collection and 2D Gel Electrophoresis

Blood samples were collected from patients by venipuncture using standard red cap glass clot tubes without accelerator or gel. Serum samples were prepared from the blood by centrifugation after standing at room temperature for 30-45 minutes. The serum supernatant was collected, aliquoted, placed on dry ice, shipped to the laboratory, and stored at −80° C. until analyzed. Sample preparation and electrophoresis were performed essentially as described previously (19-22). The first dimension electrophoresis (100 μg of serum proteins/gel) was on immobilized 11 cm IEF strips (Bio-Rad Laboratories, Hercules Calif.), pH 5-8, and in the second dimension on pre-cast 8-16% acrylamide gradient CRITERION SDS-gels (Bio-Rad Laboratories, Hercules Calif.).

Fluorescent Staining and Digital Image Analysis and Normalization

The gels were stained (Lava Purple™, Fluorotechnics; SyproRuby™, Bio-Rad Laboratories, Hercules Calif.), fluorescent digital images captured (FLA 7000 Imager Fujifilm; FX Imager, Bio-Rad Laboratories), and protein spot detection and quantitation performed (PDQUEST™, Bio-Rad Laboratories, Hercules Calif.). Spot quantities in parts per million (PPM) fluorescent pixel spot density were normalized to total gel density.

Each serum sample was analyzed in duplicate or triplicate. Quantitation of individual spots was validated for linearity, dynamic range, limit of detection (LOD=0.66 μg/ml of serum), limit of quantify ability (LOQ=6.6 μg/ml of serum), reproducibility, and robustness (CV≦20%, Supplemental Material, FIG. 1A, B, Table 1).

Biostatistical Analysis

Initial control mean concentration values for 59 selected protein biomarkers were obtained. Age matched control samples (n=78) were subjected to multiple gel runs (n=178), and the quantitative protein analysis performed on 59 protein biomarkers of neurodegenerative disease. The results for each biomarker were subjected to individual biostatistical analysis using Analyse-it Software™ imbedded in Microsoft Excel. The results were compiled to yield a standard control mean value for each biomarker, which was then employed as a constant by which all the concentration values in the study were subsequently divided, to produce a Fold of Standard Normal Mean Concentration for each data point in the study.

Statistical significance of differences in individual biomarker blood serum concentrations (as Fold of Standard Normal Mean Concentration) between patient and controls was determined by non-parametric Dot Box and Whiskers (medians) and parametric Receiver Operator Characteristics analysis using Analyze-it Software™ in Microsoft XL. Analysis of joint performance of groups of biomarkers was by multivariate linear discriminant analysis using SAS® statistical software.

Box and Whisker plots give a visual representation of non-parametric descriptive statistics. The central “box” represents the distance between the first and third quartiles (inter quartile range or IQR), with the median marked as the horizontal line inside the box. The notch in the box represents the 95^(th)% confidence interval around the median (the 50^(th) percentile); thus groups that display non-overlapping notches can be considered statistically different (p<0.05). The minimum value is the origin of the leading “whisker” and the maximum value is the limit of the trailing “whisker”. All values are plotted individually (Dots) and those values outside the whiskers are considered possible outliers, presented either as circle (far outlier) or plus sign (near outliers).

The diagnostic performance of a test or the accuracy of a test to discriminate diseased cases from normal cases is evaluated using Receiver Operating Characteristic (ROC) curve analysis. ROC curves can also be used to compare the diagnostic performance of two or more laboratory or diagnostic tests. In a ROC curve the true positive rate (sensitivity) is plotted against the false positive rate (1-specificity) for different cut-off points. Each point on the ROC plot represents a sensitivity/specificity pair corresponding to a particular decision threshold. A test with perfect discrimination (no overlap in the two distributions) has a ROC plot that passes through the upper left corner (100% sensitivity, 100% specificity). Therefore the closer the ROC plot is to the upper left corner, the higher the overall accuracy of the test.

Multivariate discriminant analysis is a well-validated multivariate analysis procedure. Discriminant analysis identifies sets of linearly independent functions that will successfully classify individuals into a well-defined collection of groups. The statistical model assumes a multivariate normal distribution for the set of biomarkers identified from each disease group.

Discriminant analysis was applied to a training set of data, from which the contribution of each individual biomarker was determined. The step disk software program (SAS®) was then used to determine the linear combinations of biomarkers that provided an optimum classification of individuals into disease groups. Alternatively, the programmer manually selected different combinations of biomarkers to be incorporated into a linear or quadratic discriminant function to optimize the classification of individuals into disease groups.

The output of discriminant analysis (DA) is a classification table that permits the calculation of clinical sensitivity (how often the test is positive in diseased patients) and specificity (how often the test is negative in non-diseased individuals. Although a DA classification table also permits the calculation of the positive predictive value (PPV) and the negative predictive value (NPV), the PPV and NPV were not assessed in this study.

Selection of Specific Proteins Related to Neurodegenerative Disease in Retrospective Patient Samples

Retrospective banked serum samples from patients with Alzheimer's disease (AD), PD, AD/PD-like and mixed disorders, amyotrophic lateral sclerosis (ALS), ALS-like and age-matched normal controls (Table 1) were analyzed by 2D gel electrophoresis, fluorescent staining, quantitative digital image analysis, and individual and multivariate biostatistics (22-25).

Fifty-nine protein biomarker spots were selected by exhaustive and painstaking comparisons of quantitative 2D gel images and individual protein concentration statistics (PPM pixel spot densities), which exhibited reproducible statistically significant abnormal, disease specific serum concentrations, differences between the following patient groups: AD vs. PD vs. ALS; AD vs. AD-like; PD vs. PD-like; ALS vs. ALS-like disorders; and familial vs. sporadic ALS. Multivariate discriminate analyses, in independent training and test sets, with combinations of sub-sets of the 59 biomarkers showed sensitivities and specificities of 85-95% (23-25).

The proteins were characterized by in-gel trypsin digestion of protein spots, peptide LC MS/MS, spot molecular weights, isoelectric points, and Edman degradation where appropriate, to identify the protein molecular entities (see FIG. 1 and Table 2). The identified biomarker proteins clustered by function into four groups: I (cellular degeneration related); II (Haptoglobin proteins); III (inflammatory proteins); and IV (albumin proteins).

TABLE 2 Initial Set of Control Biomarkers Spot Accession Func- No. No. No. Protein Identity tion 1 N5514 31615333 Chain A Albumin mutant IV R218H protein 2 N5123 4826762 Haptoglobin HP protein II 3 N5515 X1 4 N1416 4504579 Complement Factor I III 5 N3314 178849 Apolipoprotein E3 I 6 N3307 30483217 Transthyretin “dimer” protein I 7 N7007 55961147 Nucleoporin NUP 188 I 8 N2407 47124562 Haptoglobin HP-1 Protein II 9 N2511 6650826 Albumin protein PRO2044 IV 10 N6306 603553 PDLaH Parkinson's LB acidic I H2A protein (PADPR/Ub/A24) 11 N2502 37499461 Apolipoprotein A-IV I 12 N3007 48145933 TTR; HYPE Huntingtin I 37183102 Interacting Protein E 13 N7304 1314244 Complement C4b processing III product C4 gamma Chain 14 N4420 31615333 Chain A Albumin mutant IV R218H protein 15 N8301 57997594 Fidgitin I I 16 N6224 2894829 Immunoglobulin kappa light chain III 17 N4411 2144888 Complement Factor H/Hs protein III 18 N6214 57997594 Fidgitin II I 19 N3417 7770217 Albumin protein PRO2675 IV 20 N4130 X2 21 N4402 67587 Haptoglobin related protein II 22 N5302 4557325 Apolipoprotein E4 I 23 N6402 177872 Alpha-2-macroglobulin I 24 N5705 14328065 Aldehyde dehydrogenase 1 A3 I 25 N7606 6573461 Apolipoprotein H I 26 N3209 337758 Pre-serum Amyloid P protein I 27 N2412 4102235 AIM Apoptosis Inhibitor; CD5 I Scavenger Receptor 28 N5304 P46439 Glutathione S Transferase Mu5-5 I 29 N5303 52631746 Lectin 3 P35 I 30 N1308 223373 Alpha-1-microglobulin I 31 N1406 42716297 Clusterin Isoform 1 I 32 N3319 178851 Preapolipoprotein E I 33 N1514 47124562 Haptoglobin HP-1 Protein II 34 N2401 47124562 Haptoglobin HP-1 Protein II 35 N3409 47124562 Haptoglobin HP-1 Protein II 36 N2309 47124562 Haptoglobin Protein II 37 N5319 106529 Immunoglobulin kappa C III region protein 38 N6519 1335098 Hemopexin III 39 N7616 297569 Complement Factor Bb protein III 40 N7310 40786791 Complement C3c1a III 41 N7208 40786791 Complement C3c1b III 42 N7410 40786791 Complement C3c1c III 43 N9311 40786791 Complement C3c2a III 44 N9312 40786791 Complement C3c2b III 45 N1511 55648063 Complement C3dg III 46 N1506 180620 Complement cytolysis inhibitor III protein alpha subunit 47 N2307 1483187 Inter α trypsin inhibitor heavy III chain H4 related 35 KD protein 48 N4325 6650826 Albumin protein PRO2044 IV 49 N4517 31615333 Albumin protein IV 50 N5315 763431 Similar to Albumin protein IV 51 N7405 339469 Transferrin protein IV 52 N8307 339469 Transferrin protein IV 53 N5305 X3 54 N6414 X4 55 N6321 X5 56 N7320 X6 57 N1319 X7 58 N7505 X8 59 N8221 X9

Verification of Clinical Usefulness in Prospective Freshly Drawn Samples from PD Patients

A two site prospective clinical validation trial was conducted using freshly drawn samples from the University of Thessaly (56 PD patients, 30 age-matched normal controls) and Banner Sun Health Research Institute (6 PD patients and 48 age-matched normal controls). Age-matched control samples from the two sites (n=78) were subjected to duplicate or triplicate 2D gel runs (n=174, see Table 3).

TABLE 3 Combined Database of Control and PD Samples # of Gels # of Patients Control Total 174 78 PD Total 165 62

Quantitative analysis performed on the 59 protein biomarkers followed by statistical analysis of individual biomarker proteins of this control group were used to calculate the standard normal control mean values for each biomarker (see Table 4). These were employed as constants by which all the spot PPM density values were divided to convert each data point from PPM spot density to Fold of Standard Normal Mean Concentration (FSN) for each biomarker.

TABLE 4 Control N5302 > 0 17 Controls  34 gels Control N5302 = 0 61 Controls 140 gels Control for Normalization Biomarker n Mean ± % SE Control N5302 = 0 N2307 140  15.8 ± 11.3% Control N5302 = 0 N3307 140  88.2 ± 10.1% Control N5302 = 0 N3314 140 200.9 ± 3.1%  Control N5302 = 0 N4411 140  90.4 ± 10.5% Control N5302 = 0 N5123 140 1561.4 ± 8.5%  Control N5302 > 0 N5302* 34  72.6 ± 10.9% Control N5302 = 0 N5303 140 56.5 ± 3.4% Control N5302 = 0 N5304 140 81.6 ± 6.2% Control N5302 = 0 N6214 140 160.9 ± 3.3%  Control N5302 = 0 N6224 140 101.7 ± 5.7%  Control N5302 = 0 N6306 140 23.2 ± 4.5% Control N5302 = 0 N6321 140 35.4 ± 3.6% Control N5302 = 0 N6402 140 157.2 ± 6.7%  Control N5302 = 0 N6519 140 90.5 ± 5.8% Control N5302 = 0 N7208 140 30.5 ± 8.8% Control N5302 = 0 N7304 140 155.8 ± 4.3%  Control N5302 = 0 N7310 140 80.7 ± 8.8% Control N5302 = 0 N7405 140 101.2 ± 3.6%  Control N5302 = 0 N7410 140 24.9 ± 5.8% Control N5302 = 0 N7616 140 67.9 ± 6.4% Control N5302 = 0 N8307 140 67.8 ± 5.2% Control N5302 = 0 N1308 140 350.6 ± 3.2%  Control N5302 = 0 N1406 140 208.1 ± 2.4%  Control N5302 = 0 N1416 140 123.0 ± 2.9%  Control N5302 = 0 N1506 140 282.9 ± 3.7%  Control N5302 = 0 N1511 140  11.7 ± 29.7% Control N5302 = 0 N1514 140 6971.0 ± 3.4%  Control N5302 = 0 N2401 140 7664.1 ± 3.3%  Control N5302 = 0 N2407 140 4403.3 ± 4.1%  Control N5302 = 0 N2502 140 1134.2 ± 3.8%  Control N5302 = 0 N2511 140 374.9 ± 4.2%  Control N5302 = 0 N3209 140 643.4 ± 2.5%  Control N5302 = 0 N3409 140 685.6 ± 3.6%  Control N5302 = 0 N3417 140 230.6 ± 3.3%  Control N5302 = 0 N4130 140 109.2 ± 2.4%  Control N5302 = 0 N4325 140 369.3 ± 3.1%  Control N5302 = 0 N4402 140 86.8 ± 9.3% Control N5302 = 0 N4420 140 391.8 ± 4.2%  Control N5302 = 0 N4517 140  56.9 ± 13.0% Control N5302 = 0 N5315 140 402.4 ± 2.5%  Control N5302 = 0 N5319 140 38.1 ± 4.7% Control N5302 = 0 N5514 140 376.4 ± 3.6%  Control N5302 = 0 N7606 140 2279.2 ± 3.1%  Control N5302 = 0 N9311 140 51.4 ± 6.0% Control N5302 = 0 N9312 140 63.1 ± 5.9% Control N5302 = 0 N1319 140  9.7 ± 31.6% Control N5302 = 0 N2309 140 200.2 ± 7.0%  Control N5302 = 0 N2412 140 743.6 ± 4.5%  Control N5302 = 0 N3007 140 2782.6 ± 2.6%  Control N5302 = 0 N3319 140  2.0 ± 51.3% Control N5302 = 0 N5515 140 156.4 ± 8.7%  Control N5302 = 0 N5705 140 527.8 ± 8.6%  Control N5302 = 0 N6305 140  1.6 ± 29.6% Control N5302 = 0 N7007 140 347.7 ± 2.3%  Control N5302 = 0 N7505 140 478.9 ± 8.0%  Control N5302 = 0 N8221 140  8.1 ± 16.2% Control N5302 = 0 N8301 140 88.4 ± 4.4% From Classified Into Diagnosis Diagnosis PD Control Sensitivity Specificity PD 15 15 0  100% PD 31 28 3 90.3% PD 46 43 3 93.5 Control 30 2 28 93.3%

The serum concentration data (as FSN) of the 59 protein biomarkers were subjected to linear discriminant analysis (see Table 5A). A subgroup of the 59, namely 21 biomarkers, was selected by stepwise linear discriminant analysis, based on their complimentary contributions to the overall diagnostic classification of control vs. Parkinson's disease with the samples from Thessaly (Table 5B).

TABLE 5 Linear Discriminant Analysis of Patient and Control Data from Prospective Studies A. 57 Biomarkers Classified into Diagnosis From Control PD Diagnosis Specificity Sensitivity Total Control 28 2 30 93.3% 6.7% 100 PD 2 54 56 3.6% 96.4% 100 B. 21 Biomarkers Classified into Diagnosis From Control PD Diagnosis Specificity Sensitivity Total Control 28 2 30 93.3% 6.7% 100 PD 4 52 56 7.1% 92.9% 100 C. Additional Patients Classified into Diagnosis From PD Diagnosis Control Sensitivity Total PD 0    6   6 0% 100% 100 Additional Patients: Six serum samples from PD patients from the US correctly classified as PD obtained by the same discriminant function that was used in B, i.e., 21 biomarkers and trained by the database of patients and controls from Greece.

The linear discriminant function of each of the 21 selected biomarkers for the PD patients and the age-matched controls is given in Table 6.

TABLE 6 Linear Discriminant Function for Controls and Patients Linear Discriminant Function for PD Variable Control PD Protein ID Constant −23.71 −23.98 N5514 2.2395 −0.725 Chain A Albumin Mutant R218H Protein N5123 1.2611 1.7463 Haptoglobin HP-2a Protein N5515 1.006 0.2698 X1 Protein N1416 3.877 7.0068 Complement Factor I Protein N3314 5.64 2.8679 Apolipoprotein E3 Protein N3307 −0.07 −1.395 Transthyretin “Dimer” Protein N7007 5.7858 7.9373 Nucleoporin NUP188 Protein N2407 −0.692 0.4279 Haptoglobin HP-1 Protein N2511 3.5827 1.6327 Albumin Protein PRO2044 N6306 1.2861 0.0203 PDLaH acidic H2A (ADPR/ub/A24) Protein N2502 6.3247 4.6238 Apolipoprotein A-IV Protein N3007 3.3422 6.323 Transthyretin HYPE Protein N7304 2.2082 2.7762 Complement C4b Gamma Chain Protein N4420 1.3369 2.154 Chain A Albumin mutant R218H Protein N8301 4.7277 3.2318 Fidgitin Protein I N6224 −0.191 −1.053 Immunoglobulin Kappa Light Chain Protein N4411 −0.259 0.0751 Complement Factor H/Hs Protein N6214 6.4806 7.6981 Fidgitin Protein II N3417 1.4272 −0.253 Albumin Protein PRO2675 Protein N4130 −2.076 −0.61 X2 Protein N4402 0.1899 0.4446 Haptoglobin Related Protein

The 21 biomarkers selected by stepwise linear discriminant analysis as the optimal complimentary biomarker set contributing to the overall diagnostic classification of control vs. Parkinson's disease are identified as Nos. 1-21 in Table 2 and in Table 7B. When the resulting discriminant function was used (see Tables 6-8), 28 of 30 controls from Thessaly scored as controls (specificity=93.3%), and 52 of 56 PD patients from Thessaly scored as PD (sensitivity=92.9%). The six patients from Sun City that were not used in the generation of the discriminant function were all correctly classified.

TABLE 7 Summary Statistics for 21 Biomarkers A: Summary Statistics: Average Squared Partial Wilks' Pr < Canonical Pr > B: Biomarker protein Identities: Step Label R-Square F Value Pr > F Lambda Lambda Correlation ASCC Biomarker Protein ID 1 N5514 0.1019 26 <.0001 0.8981 <.0001 0.1019 <.0001 N5514 Chain A Albumin Mutant R218H Protein 2 N5123 0.0594 14.39 0.0002 0.8447 <.0001 0.1553 <.0001 N5123 Haptoglobin HP-2a Protein 3 N5515 0.0592 14.3 0.0002 0.7947 <.0001 0.2053 <.0001 N5515 X1 Protein 4 N1416 0.0538 12.86 0.0004 0.7519 <.0001 0.2481 <.0001 N1416 Complement Factor I Protein 5 N3314 0.0432 10.16 0.0016 0.7194 <.0001 0.2806 <.0001 N3314 Apolipoprotein E3 Protein 6 N3307 0.0435 10.18 0.0016 0.6881 <.0001 0.3119 <.0001 N3307 Transthyretin “Dimer” Protein 7 N7007 0.0444 10.37 0.0015 0.6576 <.0001 0.3424 <.0001 N7007 Nucleoporin NUP188 Protein 8 N2407 0.0382 8.82 0.0033 0.6324 <.0001 0.3676 <.0001 N2407 Haptoglobin HP-1 Protein 9 N2511 0.052 12.13 0.0006 0.5995 <.0001 0.4005 <.0001 N2511 Albumin Protein PRO2044 10 N6306 0.0316 7.18 0.0079 0.5806 <.0001 0.4194 <.0001 N6306 PDLaH acidic H2A (ADPR/ub/A24) Protein 11 N2502 0.0261 5.87 0.0163 0.5654 <.0001 0.4346 <.0001 N2502 Apolipoprotein A-IV Protein 12 N3007 0.0291 6.54 0.0113 0.549 <.0001 0.451 <.0001 N3007 Transthyretin HYPE Protein 13 N7304 0.0248 5.52 0.0196 0.5353 <.0001 0.4647 <.0001 N7304 Complement C4b Gamma Chain Protein 14 N4420 0.0233 5.14 0.0243 0.5229 <.0001 0.4771 <.0001 N4420 Chain A Albumin mutant R218H Protein 15 N8301 0.0179 3.93 0.0488 0.5135 <.0001 0.4865 <.0001 N8301 Fidgitin Protein I 16 N6224 0.0159 3.45 0.0646 0.5054 <.0001 0.4946 <.0001 N6224 Immunoglobulin Kappa Light Chain Protein 17 N4411 0.0173 3.75 0.0543 0.4966 <.0001 0.5034 <.0001 N4411 Complement Factor H/Hs Protein 18 N6214 0.0147 3.17 0.0764 0.4893 <.0001 0.5107 <.0001 N6214 Fidgitin Protein II 19 N3417 0.0115 2.46 0.1186 0.4837 <.0001 0.5163 <.0001 N3417 Albumin Protein PRO2675 Protein 20 N4130 0.0149 3.17 0.0766 0.4765 <.0001 0.5235 <.0001 N4130 X2 Protein 21 N4402 0.0134 2.83 0.0937 0.4701 <.0001 0.5299 <.0001 N4402 Haptoglobin Related Protein

Of the PD patients for which symptom severity was measured, 15 of 15 patients with mild PD (Hoehn and Yahr scale=1-2; UPDRS=13.7±4.9 SD), scored as PD (sensitivity 100%, see Table 8), and 28 of 31 patients with moderate to severe PD (Hoehn and Yahr scale=2.5-5; UPDRS=26.6±9.1 SD) scored as PD (sensitivity 90.3%, see Table 8).

TABLE 8 Probability of PD Diagnosis vs. Disease Severity Sensitivity and Specificity of diagnosis by Linear Discriminant Analysis with 21 Biomarkers Severity of PD Classified Symptoms From Into Diagnosis Hoehn & Diagnosis PD Control Sensitivity Specificity Yahr Scale* PD 15 15 0  100% Mild (HY 1-2) PD 31 28 3 90.3% Moderate-Severe HY (2.5-5) PD 46 43 3 93.5 Total PD (HY 1-5) Control 30 2 28 93.3% Control *HY 1-2: UPDRS 13.7 ± 4.89; HY 2.5-5: UPDRS 26.6 ± 9.07; (Avg. ± SD) ¥All patients under treatment with levodopa or other dopamine agonists ^(†)Sensitivity and specificity by Receiver Operator Characteristics (ROC) of posterior probability of membership in diagnosis: >0.3707 = PD.

When the posterior probabilities of Parkinson's disease (PD-P) from discriminant analysis were compared by Dot, Box, and Whiskers graphs (FIG. 2A), and Receiver Operator Characteristics (ROC) plots (FIG. 2B), both PD groups showed marked separation from the controls (FIGS. 2, 3). Of the 21 selected proteins, 11 showed individual statistically significant abnormal concentrations in the patients with mild, and 14 with moderate-severe PD symptoms (FIG. 3, Table 9). Use of the concentrations of all 59 of the biomarkers in linear discriminant analysis provided a modest increase in sensitivity for PD diagnosis beyond using the 21 biomarker set (Table 5: A, 96.4% vs. B, 92.9%).

TABLE 9 Receiver Operator Characteristics of Fold of Standard Mean Concentration Values per Patients vs. Severity of PD Symptoms Moderate to Mild = HY 1-2 Severe = HY 2.5-5 Biomarkers HY 1-2 >/< Ctrl AUC SE ROC-P HY 2.5-5 >/< Ctrl AUC SE ROC-P N5514 <Ctrl 0.70 0.049 <0.0001 <Ctrl 0.68 0.042 <0.0001 N5123 >Ctrl 0.68 0.052 <0.0004 >Ctrl 0.74 0.039 <0.0001 N5515 <Ctrl 0.65 0.046 <0.0007 N1416 >Ctrl 0.63 0.054 <0.010 >Ctrl 0.58 0.056 <0.040 N3314 <Ctrl 0.64 0.055 <0.0050 <Ctrl 0.66 0.043 <0.0002 N2407 >Ctrl 0.67 0.042 <0.0090 >Ctrl 0.63 0.055 <0.0001 N2511 <Ctrl 0.66 0.057 <0.004 <Ctrl 0.65 0.044 <0.0003 N6306 <Ctrl 0.62 0.058 <0.03 <Ctrl 0.60 0.044 <0.010 N3007 >Ctrl 0.64 ± 0.056 <0.0080 N4420 <Ctrl 0.65 0.057 <0.005 N8301 <Ctrl 0.60 0.045 <0.014 N6224 <Ctrl 0.62 0.054 <0.02 <Ctrl 0.60 0.045 <0.020 N4411 >Ctrl 0.62 0.044 <0.003 N3417 <Ctrl 0.68 0.054 <0.0006 <Ctrl 0.66 0.043 <0.0001 N4130 <Ctrl 0.58 0.045 <0.050 N4402 <Ctrl 0.63 0.044 <0.002 The 21 PD-P > 0.3701 0.99 0.009 <0.0001 PD-P > 0.3701 0.97 0.025 <0.0001 Combined^(‡)

These results demonstrate that rigorous 2D gel technology coupled with image analysis can identify a collection of 59 serum proteins that were abnormally expressed in neurodegenerative diseases when compared to controls in banked, retrospectively analyzed samples. A novel investigation of these 59 biomarkers using a novel statistical approach has found that 21 of the protein biomarkers optimally classified PD and normal samples in prospectively collected samples when a multivariate discriminant analysis was applied to this set. When the same discriminant function was applied to the 2D gel data from a small test set of PD samples from an independent site that were not used for the determination of the function, it correctly identified all 6 of them.

The identities of these 21 protein biomarkers are consistent with mechanisms related to neuronal degeneration that are understood to be active in PD (FIG. 3, Table 2). The 36 remaining proteins of the 57 protein group still displayed substantial, statistically significant abnormal concentrations in PD patient sera. Like the 21 proteins, the 36 proteins are represented in the same four functional groups, and so add to the understanding of PD mechanisms. For some proteins, only specific isoforms or variants were among the 21, whereas the others were among the 36 not selected, for instance Apolipoprotein E3 was selected in the 21 biomarkers, while Apolipoprotein E4 was not selected; Haptoglobin HP-1c was selected, while Haptoglobin HP-1a, b, d, and e, were not selected; Immunoglobulin κd was selected, while Immunoglobulin κc was not selected; and Albumin PRO20444 was selected, while Albumin PRO2044-II not selected.

REFERENCES

All patents and publications mentioned in this specification are indicative of the level of skill of those of knowledge in the art to which the invention pertains. All patents and publications referred to in this application are incorporated herein by reference to the same extent as if each was specifically indicated as being incorporated by reference and to the extent that they provide materials and methods not specifically shown.

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1. A method of diagnosing Parkinson's disease, the method comprising: collecting a serum sample from a test subject; analyzing the serum sample for a change in expression of a set of protein biomarkers; and using the change in expression of the set of biomarkers to diagnose the test subject.
 2. The method of claim 1, wherein the set of biomarkers includes twenty-one serum proteins.
 3. A set of biomarkers for diagnosing Parkinson's disease, wherein a change in the expression of the set of biomarkers in a serum sample of a test subject determines a positive or a negative diagnosis of Parkinson's disease in the test subject.
 4. The set of biomarkers of claim 3 comprising: Chain A Albumin mutant R218H protein, Haptoglobin HP-2a protein, Complement Factor I protein, Apolipoprotein E3 protein, Transthyretin dimer protein, Nucleoporin NUP 188 protein, Haptoglobin HP-1 protein, Albumin protein PRO2044, Parkinson's LB acidic H2A protein, Apolipoprotein A-IV protein, Transthyretin Huntington Interacting Protein E, Complement C4b gamma chain protein, Chain A Albumin mutant R218H protein, Fidgitin I protein, Immunoglobulin kappa light chain protein, Complement Factor H/Hs protein, Fidgitin II protein, Albumin protein PRO2675, and Haptoglobin related protein.
 5. A method for diagnosing Parkinson's disease comprising: obtaining a serum sample from a test subject; electrophoresing a portion of the serum sample on a 2D gel, wherein the portion of the serum sample has a standardized protein concentration; determining a pixel cell density for each of at least twenty-one protein biomarkers in the 2D gel; and comparing the pixel cell density of each biomarker with a mean cell pixel density determined for control serum; whereby a variation in the pixel cell density of at least twenty-one biomarkers from a mean pixel cell density of the at least twenty-one biomarkers is a positive diagnosis of Parkinson's disease. 